BigQuery ML Concepts & Examples: Starter Guide

google cloud bigquery ml

BigQuery ML is a machine learning platform that allows data scientists to build models using the power of their data. Unlike traditional machine learning, BigQuery ML does not require any programming skills, making it an easy way to get started with machine learning. Product managers and data scientists can both benefit from BigQuery ML by finding insights in their own datasets or collaborating with one another on new applications.

The introduction of BigQuery Machine Learning Platform has enabled organizations to take advantage of the benefits of machine learning without needing deep expertise in either big-data or analytics technologies. This blog post will provide an overview of what you need to get started with Big Query Machine Learning Platform and how product managers and data scientists can use it for their work!

What is BigQuery ML?

BigQuery ML is a Google cloud machine learning service which enables you to build and operationalize machine learning (ML) models on structured or semi-structured data, directly inside BigQuery, using simple SQL and without writing any programming language code (such as Python, R or Java). The advantage that BigQuery ML brings is SQL like syntax which enables anyone with the knowledge of SQL to get started with BigQuery ML. This includes product managers, data analysts and data scientists. Bigquery ML brings machine learning to the data. Unlike traditional machine learning, BigQuery ML does not require knowledge of any programming skills including that of Python / R / Scala thereby making it an easy way to get started with machine learning.

What are benefits of BigQueryML?

Bigquery ML can be very beneficial for product managers, data analysts and data scientists to get started with building machine learning models by uploading the data into Bigquery platform.

Benefits of using Bigquery ml for product managers

  • Extract insights from the existing dataset or combine the datasets with other datasets. This can enable them to come up with new data-driven product features. It can enable them to make better decisions regarding new products or features they wish to introduce into the market. It can help them build products related to forecasting future events such as demand or pricing decisions across different geographical regions.
  • Build machine learning models by having decent knowledge around SQL. The biggest advantage of Bigquery ML is the ease of access to machine learning algorithms through a simple SQL like syntax.
  • Do not need programming skills such as Python, R, Scala or Java or deep expertise about machine learning algorithms to get started.
  • Do not need to worry about the machine learning and data infrastructure

Benefits of using Bigquery ml for data scientists / data analysts

  • Data Scientists can use BigQuery ML to build models in the cloud without any hassle of deploying, maintaining and scaling servers for their machine learning model training tasks. These models can also be shared with other team members by exporting the models. The bigquery ML models can be exported and deployed on AI platforms or local machines. The tutorials for building models for different kinds of problems can be found on this tutorial page.
  • Perform hyperparameter tuning to improve the model performance
  • Use features such as TRANSFORM to perform feature engineering tasks.
  • Easily create and run machine learning models on large datasets without worrying about data and machine learning infrastructure.
  • As like product managers, don’t need to be expert with programming languages such as Python, R, Scala or Java to build machine learning models.

What are different types of models which can be built using Bigquery ML?

BigQuery ML models can be classified into two different categories such as the following:

  • Built-in models: BigQuery ML built-in models are trained within BigQuery, such as linear regression, logistic regression, kmeans, matrix factorization, and time series models.
  • External models: BigQuery ML external models are trained utilizing other Google Cloud services, DNN and boosted tree models (trained using Vertex AI) and AutoML models (trained using the AutoML tables).

The following types of models are supported by Bigquery ML:

  • Linear regression for numeric prediction such as stock price, payment delay days, sales on a particular day
  • Binary and multiclass (more than two classes of labels) logistic regression for classfication problems
  • K-means clustering for identifying data clusters
  • Matrix factorization for product recommendation. Recall that matrix factorization is used for collaborative filtering, i.e., matrix factorization to predict the preference of users for movies or products
  • Time series forecasting for forecasting future events such as demand or pricing decisions. The model automatically handles anomalies, seasonality, and holidays.
  • Unsupervised anomaly detection and non-linear dimensionality reduction using Autoencoder trained in TensorFlow.
  • XGBoost for classification and regression problems
  • Automl tables for searching through a variety of model architectures to decide the best model.

How does the Bigquery ML syntax look like?

The following are some common Bigquery ML commands:

  • CREATE MODEL: CREATE MODEL statement creates, trains and saves a new machine learning model from dataset or from one of its views/tables referenced by this command. If no FROM clause is specified, the model is created from a view or table that has been referenced earlier in this statement.
  • ML.EVALUATE: This statement enables you to evaluate a machine learning model created with BigQuery ML CREATE MODEL command by specifying the split of data at which it should be trained and tested.
  • ML.PREDICT: ML.PREDICT statement allows you to predict output for a new observation based on existing model.

How to get started with Big Query Machine Learning Platform?

One would need the following to get started with bigquery ML service:

  • A Google Cloud Platform Project
  • Dataset with a view or table to create the model from. A new dataset can also be created if necessary.
  • If you are using the standard SQL dialect, it is important to specify column types.
  • Here is a list of how-to guides in relation to bigquery ML.

Get to know the pricing of BigqueryML service on this pricing page. Note that the first 10 GB of data processed by queries that contain CREATE MODEL statements per month is free. BigQuery ML model training pricing is based on the model type (built-in models vs external models) as well as the usage pattern such as flat-rate or on-demand. BigQuery ML prediction and evaluation functions are executed within BigQuery ML for all model types.

It’s no surprise that machine learning has become an integral part of any company looking to be competitive in the big data space. Data scientists and product managers alike are leveraging this powerful tool for their respective purposes, but they often require different levels of expertise with programming languages like Python or Scala. BigQuery ML is a way for anyone who needs predictions to get started without having to worry about complicated coding – it does all the heavy lifting behind-the-scenes.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking
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